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Statistical and Computational Trade-Offs in Kernel K-Means
Daniele Calandriello, Lorenzo Rosasco
More precisely, we study a Nyström approach to kernel k-means. Weanalyze thestatistical properties oftheproposed method andshow that it achieves the same accuracy of exact kernel k-means with only a fraction of computations.
Generating Diverse High-Fidelity Images with VQ-VAE-2
Ali Razavi, Aaron van den Oord, Oriol Vinyals
Neural Information Processing Systems http://nips.cc/
Variational Gaussian Processes For Linear Inverse Problems
By now Bayesian methods are routinely used in practice for solving inverse problems.
Universality and individuality in neural dynamics across large populations of recurrent networks
Niru Maheswaranathan, Alex Williams, Matthew Golub, Surya Ganguli, David Sussillo
Or alternatively, are the learned dynamics highly sensitiveto different model choices?
Gradient Descent for Spiking Neural Networks
Dongsung Huh, Terrence J. Sejnowski
Neural Information Processing Systems http://nips.cc/
PALMER: Perception-ActionLoopwithMemory forLong-HorizonPlanning
This creates a tight feedback loop between representation learning,memory,reinforcementlearning,andsampling-basedplanning.
